This invention relates to a computer system and method for forecasting the traffic at checkout lanes in mass merchandising stores, department stores, grocery stores, and other applications to give management sufficient time to properly staff the checkout lanes to maximize labor efficiency and customer satisfaction.
Traditionally, the systems used by store management to staff checkout lanes have been "reactive". By visually observing the length of the checkout lines management would adjust the number the checkout personnel as needed. There are a number of problems associated with this approach. The store manager does not always notice an increase in lines until after customers have experienced excessive waiting in the lines. The result is customer dissatisfaction with the store and a substantial likelihood of lost sales. There appears to be a direct relationship between the number of purchases a customer will make and the length of the checkout lines. There also appears to be a relationship between whether a customer will shop in a particular store and the length of the store's checkout lines. Of course, the problem can be alleviated by over staffing the checkout lanes, but this results in a waste of store personnel and increased overhead that reduces profits. Moreover, in many stores checkout personnel have other duties when they are not working at the registers, so even when the manager observes excessive checkout lines, it takes some period of time to bring additional employees on to the registers from their present duties. In the meantime, customers become irritated and dissatisfied with a potential loss of business.
Another problem experienced with the reactive approach is that from a customer relations point-of-view it is easier for the store management to open checkout lanes than to then close them. Once a lane is open, management tends to keep the lane open for some period of time even if lane traffic diminishes resulting in an inefficient use of store personnel.
The checkout lane alert system of the present invention overcomes these problems by providing a "proactive" computer system and method that predicts lane traffic in the store and gives the store manager advance notice so that adjustments in checkout lane staffing can be made that will prevent excessive checkout lines and excessive checkout lanes. So rather than wait for the lines to begin building, and then reacting to the build up, the system and method of this invention forecasts the staffing requirement and allows proper staff deployment before a build up begins. While traditional methods are mostly static in that staff levels are predetermined, the system of this invention allows for dynamic staffing of the checkout lanes for more efficient allocation of labor.
The checkout lane alert system of the present invention is a live, computer-based, in-store system that integrates real-time shopper traffic data with computerized statistical analysis in order to generate accurate short term forecasts of shopper traffic at the checkout lanes of the store. The system uses up to the minute traffic data to create its forecast and allows a retailer to track the momentary surges in lane traffic and meet these with the proper staffing.
Generally, the system of the present invention comprises a person and object recognition system component and a computer system running appropriate software. The recognition system "recognizes" the potential shoppers as they enter and leave the store by recognizing persons or objects as they move past a selected location in the store and classifying the persons or objects in accordance with selected criteria. Such a recognition system is disclosed in Frey, U.S. Pat. No. 5,138,638, the entirety of which is incorporated herein by reference, and is further disclosed in U.S. Pat. application Ser. No. 07/855,503, filed Mar. 20, 1992, entitled "Person and Object Recognition System", (the entirety of which is incorporated herein by reference) which is a continuation-in-part of the application which issued into U.S. Pat. No. 5,138,638. The two components are connected by a cable that allows communication of the shopper traffic data from the recognition system to the computer in realtime.
The computer that is connected with the recognition system runs the software for the present invention. This software allows the computer to retrieve the shopper entry and exit time data from the recognition system for use in its statistical analysis. The software combines this real-time data with pre-gathered statistical data about the population that shops in a particular type of store, and data which characterizes the checkout lane throughput capability for a store. Each minute the software performs many simulations which combine these factors in different ways in order to forecast the shopper traffic at the checkout lanes for that minute and minutes that follow. The computer screen graphically displays the forecast, and the system updates the screen display with results of the simulations and alerts store personnel when lane traffic will increase or decrease to a point where a new checkout lane staffing level is needed.
The system of the present invention uses two pieces of information about the shopping population in the store. The first is called the "conversion rate" which is the ratio of shoppers who actually buy an item or items to the total shoppers in the store. This information is used to estimate the percentage of the total entering shoppers that will actually visit the checkout lanes. The second piece of information concerns the amount of time the shopper spends in the store. This information allows the system to forecast the arrival time of each shopper at the checkout lane. The system uses a frequency distribution of the shopping times of shoppers in the store. During each simulation performed by the system, shoppers are assigned pseudo-random shopping times based on the frequency distribution. During the many repetitions of the simulation, shoppers will be assigned different random combinations of shopping times and the final forecast is the average of the simulations.
The system also uses "optimal" service criteria for a particular store and a frequency distribution of shopper checkout times (how long it takes for a shopper to check out once the shopper reaches the register) to determine the number of checkout lanes required once the lane traffic is forecast. The frequency distribution of checkout times is pre-gathered automatically from the store point-of-sale (POS) checkout equipment.